Cloud-based analytical platform for health data pattern and trend analysis

ABSTRACT

A cloud-based analytical platform for health data pattern and trend analysis is configured to: receive, from a transmitter of a portable device, processed data derived from physiological data measurements gathered from sensors worn by a user, the sensors being communicatively coupled to the portable device; analyze the received processed data; and transmit the results of the analysis to at least one of the portable device and an authorized healthcare entity. The analyzing of the received processed data comprises using multi-parameter machine learning algorithms to automatically derive the current state of one or more physiological vital parameters and related health conditions for an individual user; automatically deriving deviations from baseline for each of the one or more physiological vital parameters of the user; and automatically deriving a long-term trend for each of the one or more physiological vital parameters of the user.

CROSS-REFERENCE

This application claims priority to U.S. application Ser. No.15/636,073, filed Jun. 28, 2017, which in turn claims priority toProvisional Application No. 62/355,507, filed Jun. 28, 2016, both ofwhich are incorporated herein by reference in their entirety, as if setforth in full in this application for all purposes.

OVERVIEW

A non-invasive multi-sensor eco-system tracks and monitors criticalhuman physiological parameters, including those covered by the term“vital signs,” to detect and predict health conditions. The system maybe operated in an adaptive mode. The physiological parameters areextracted from a plurality of sensors using novel algorithms. Theparameters measured by one embodiment may include blood pressure, heartrate, oxygen saturation (SpO2), respiratory rate, blood glucose level,body temperature and physical activity measured as step count.

The eco-system consists of multiple components wirelessly communicatingwith each other: (1) wearable sensors, which may include signalprocessing functionality as well as wireless inter-sensor communicationand short-term data storage; (2) a portable computing device hosting amobile application which enables reception of the processed sensed data,transmission of that data to a cloud platform for analysis, display ofpush notifications determined by the processed sensed data, reception ofanalysis results fed back from the cloud platform, and visualization ofthe processed sensed data and of the cloud analytics data; and (3) thecloud platform itself, allowing long-term data storage as well asanalysis of the measurement data to obtain short and long-term healthtrends and future health predictions. In some embodiments the eco-systemalso includes a linked healthcare provider, for professional review andaction as and when necessary or appropriate.

The eco-system operates to (a) analyze the physiological parametersderived from data provided by two or more sensors, positioned atdifferent locations over the subject's body; (b) compare them againsttheir respective normal, critical and life-threatening bounds as definedby the clinical community; and (c) provide feedback, alerts, pushnotifications and/or 911 calls depending on the criticality of theresults of the comparison. Machine learning algorithms may be employedto carry out various aspects of the analysis, at the cloud platformlevel.

BACKGROUND

With the increase in the size of the elderly population, as well as theemergence of chronic diseases on a broader population segment, largelyinfluenced by changes in modern lifestyle, coupled with rapid increasein healthcare costs, there has been a significant need to monitor thehealth status and overall wellbeing of individuals in their dailyroutine to prevent serious health disorders. Alongside, we observe anincrease in thirst for quantification of one's own health on acontinuous basis. The adoption of mobile healthcare technology promisesto enhance the quality of life for chronic disease patients and theelderly, as well as healthy individuals. Furthermore, it offers thepotential to alter the modality of the current healthcare system byenhancing the scope of out-patient care and by reducing the need forhospitalizations and other cost-intensive healthcare needs.

Some solutions have been proposed to address issues in this area, butnone of them has provided a closed and comprehensive eco-system asenvisaged by the present invention.

There is, therefore, a need for systems and methods that allow forcontinuous non-invasive health monitoring technology—a disruptivetechnology, in the sense that it would alter the perspective ofhealthcare from reactive to proactive. The eco-system would ideally beclosed-loop and comprehensive, covering a spectrum of actions, fromautomatically collecting physiological parameters from each of aplurality of users, getting a full understanding of the parameterprofile for each individual user, and recording their long-term healthtrends and conditions, to providing guidance toward attaining ahealthier lifestyle for individual users, groups and the community as awhole.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a high level view of an eco-system according to oneembodiment of the invention.

FIG. 2 schematically illustrates the functioning of an eco-systemaccording to one embodiment of the invention.

FIG. 3 illustrates examples of sensors that may be worn in variousembodiments of the invention.

FIG. 4 illustrates two examples of subjects wearing sensors according toembodiments of the invention.

FIG. 5 illustrates the computational flow of data through someembodiments of the invention.

FIG. 6 illustrates low power connectivity between hardware elements insome embodiments of the invention.

DETAILED DESCRIPTION

The manner in which the present invention provides its advantages oversystems in current use can be more easily understood with reference toFIGS. 1 through 4. It should be noted that throughout this disclosure,the words “user”, “patient”, and “subject” are used interchangeably.

FIG. 1 is a high level view of an eco-system 100 of the presentinvention, illustrating relationships between four majorelements—sensors 110 (central sensor 110A and just one remote sensor110B are shown in this example for simplicity, but in other embodiments,there may be additional remote sensors), a cloud platform 130 hostingAI-based analytics, and a mobile or portable device 105. Device 105 hasa user interface enabling communication with the sensors, the platform,and with an entity 120, typically comprising a healthcare entity, whichmay, for example, be a physician, a clinic, or an emergency care unit.Entity 120 may also include a user chosen sub-community of people suchas family members. These various elements make up a closed orself-contained, independently functioning eco-system, which in thisembodiment includes entity 120. In some embodiments, entity 120 may beconsidered to lie outside the eco-system, but to be in communicationwith it. In the embodiment shown in FIG. 1, a single healthcare entity120A is communicatively coupled to mobile device 105 and directly orindirectly to cloud platform 130. In another embodiment, not shown,there may be two or more different healthcare entities, one incommunication with the cloud platform and the other in communicationwith the mobile device.

FIG. 1 indicates how a system of continuous and adaptive vital datamonitoring with clinical accuracy may result in a healthier lifestyleand peace of mind.

FIG. 2 illustrates the functioning of elements of an eco-system 200according to the present invention, showing a finer granularity levelthan FIG. 1, and illustrating some of the steps performed by componentsof the closed-loop ecosystem.

One element or category is a plurality of wearable sensors (110 in FIG.1), including one central sensor and one or more auxiliary or remotesensors worn by a user. Each sensor is configured to monitor one of theuser's physiological parameters. Examples of typical parameters ofinterest are listed in Table 1.

TABLE 1 1. Heart Rate 2. Pulse Rate 3. Heart Rate Variability 4. CardiacIndex 5. Blood Pressure 6. Blood Glucose 7. Respiratory Rate 8. OxygenSaturation (SpO2) 9. Desaturation Index 10. Apnea Hypopnea Index 11.Body Temperature 12. Electrocardiograph Activity 13. Electro DermalActivity

As shown in FIG. 2, measurements of one or more of these parameters maybe initiated at step 240, as and when desired by the user, using aninterface of an application on a convenient portable device easilyaccessed by the subject, such as a smart-phone (105 in FIG. 1).Alternately, the parameters may be automatically measured as programmedin such an application. Components of device 105 include receiver 106,transmitter 107, processor 108, and display screen 109.

The central sensor is typically worn on the wrist; typical locations forother sensors include the forehead, chest, fingertip, earlobe and leg.Examples of measurement technologies used include photoplethysmography(PPG), electrocardiography (ECG), 3-axis accelerometry, temperaturemeasurement using thermistors, and electrodermal activity monitoring.Some of the sensors (often those at the forehead, earlobe and fingertip)may be used primarily or solely to provide calibration signals for othersensors.

FIG. 3 shows close up views of examples of sensors at their envisagedbody locations. Sensor 310A is a wrist-mounted sensor, typically thecentral sensor of the system. Sensors 310B, 310C, 310D, 310E, and 310Fare examples of sensors designed to be worn on “remote” locations suchas a finger tip, earlobe, around the chest, head, or ankle respectively.

FIG. 4 illustrates how such sensors may be worn by two subjects atdifferent stages of life. The wireless communication of data between thecentral sensor and each remote sensor may be carried out using Bluetoothor other well-known and established wireless technologies. The placementof sensors 310A-F is shown on the youthful figure on the left, while thecorresponding physiological parameters that may be measured using thosesensors are shown on the elderly figure on the right. In differentembodiments of the invention, a subset of the sensors shown may be used,with as few as one remote sensor present in addition to one centralsensor.

In some embodiments, a single sensor may provide data indicative of morethan one physiological parameter of interest. One example of this is aphotoplethysmographic (PPG) sensor, which essentially monitors bloodvolume, but from which data indicative of SpO2, glucose, heart rate,blood pressure, and respiratory rate may be derived. Sensors may beoperated to monitor the wearer's vital parameters continuously andautomatically over long periods of time.

Returning to FIG. 2, once the measurement instructions are issued atstep 240 and received by the central sensor at step 242, central sensorselects at step 244 which sensor or sensors are required to perform thedesired measurement or measurements. If necessary, the request istransmitted to a remote sensor at step 246. Each designated sensor(central or remote) performs the measurements of the correspondingparameter or parameters at steps 248 and/or 250 respectively. Anymeasurements performed by remote sensors (such as a leg sensor forexample) are wirelessly sent to the central sensor (typically the sensorworn on the subject's wrist), with that central sensor takingresponsibility for aggregating the other sensors' data as and whennecessary, processing them at step 252, as will be described in greaterdetail below, and transmitting the results to the user interface on themobile device, typically a smart-phone.

Calibration plays an important role in attaining clinical-grade accuracyfor all measurements of physiological parameters. Two methods may beused to address the calibration issue:

1. Static calibration: Measurement of a parameter using the proposedapparatus is compared against the gold standard (clinical settingmeasurement) and repeated for a large and diverse set of individuals.The measurement error computed is used to determine the calibrationcoefficients for the given parameter. The calibration coefficients thusobtained are applied to every apparatus manufactured. The calibrationcoefficients do not change for the lifetime of operation for a givenapparatus.

2. Dynamic AI-based calibration: The calibration coefficients of a givenparameter are dynamically computed on cloud platform based on data froma large population bucketed according to age, sex, race, skin color,skin thickness etc. As new data points get added into a specificpopulation bucket, the calibration coefficients get recomputed andadjusted into the settings of a given apparatus used by an individual.The calibration coefficients in this method get constantly adjusted andimproved over the lifetime of the apparatus or device performing theparameter measurement of interest.

The second method, dynamic calibration, clearly provides somesignificant advantages in terms of specificity for the individual, andlong term reliability. In the present invention, both staticcalibration—the current standard practice—and dynamic calibration may beused, to provide a desirable combination of accuracy, convenience,specificity and reliability.

Returning to FIG. 2, at step 252, as noted above, the central sensorprocesses (filters, calibrates, scales, etc) the raw data received togenerate physiological parameter data with accuracies sufficient torender the parameter data clinically meaningful. Specially developedhardware-embedded algorithms may be used to achieve real-time signalprocessing. FIG. 5 schematically illustrates the computational flow ofdata gathered by various sensors, and processed by hardware-embeddedalgorithms according to some embodiments of the present invention, toyield data of clinical significance. The central sensor then comparesthose processed data values to values defining ranges of interest(normal, critical and life-threatening) for each correspondingparameter, at step 254. The measured and processed data may be storedfor the short term in the memory of the central sensor, at step 256.Depending on the results of the comparisons, the central sensor maywirelessly send push notifications to the smart-phone (or similarportable device). These notifications may be normal text, or in someembodiments, simple symbols or easily appreciated codes. For example, atstep 258, a blue code, or a predetermined symbol such as a smiley facemay be sent to indicate to the subject that a parameter is within normalbounds, an orange code may be sent at step 260 to indicate that theparameter is outside normal bounds but within critical bounds, or a redcode may be sent at step 262 to indicate life-threatening bounds havebeen exceeded. In some embodiments, audible alerts may be issued as wellas or instead of visible ones. In the case of a life-threateningsituation (red alert), the smart-phone may automatically initiate a 911call.

In some embodiments, not shown in this figure, alerts may be sent tomedical professionals such as the user's personal physician, or tohealth centers or emergency services. In less serious cases, alerts maybe sent just to the user, accompanied by recommendations on relevantcorrective actions.

One advantage of the present invention is that the data processing andtransmission burdens of the entire group of sensors is carried by justthe central sensor, easing the power consumption and size, weight,complexity and cost demands on the remote sensors.

FIG. 6 schematically illustrates one embodiment in which ultra shortrange (0.5 m to 1 m), ultra low power (1 to 10 microwatt range)Bluetooth wireless connections are provided between central sensor 610Aand five remote sensors 610B-F, and a slightly longer range (1.5 m) lowpower (100 microwatt to 300 microwatt) Bluetooth connection is providedbetween central sensor 610A and mobile (in this case hand-held) device605. In other embodiments, other similar low and ultra low powerprotocols may be used. Reduced power consumption results in longerbattery lifetime and reduced device heating, so better reliability.

At step 264, the smart-phone (or other portable device) then uses thestandard internet service (e.g. 4G, LTE, WiFi etc) to securely send theprocessed data to the cloud for long-term storage and analytics as willbe described further below. It should be noted that the use of just onedevice—the smart-phone or similar device—to handle the transmission ofprocessed data to the cloud significantly simplifies system design andpower consumption relative to the situations common today, where eachsensor of a plurality worn by a subject independently processes andtransmits data to distant receivers. In the present invention, theremote sensors only have to transmit data over very short distances toreach the central sensor, which then sends processed data to thesmart-phone, which in turn transmits them to the cloud, and receivesother data (such as trend data discussed below) back. The display screenon the smart-phone (or PDA or tablet) allows the subject to receive pushalerts and easily visualized displays of the results of the cloud-basedanalytics.

The cloud provides long term storage of the data received from thesmart-phone, and carries out analysis using conventional and/or machinelearning algorithms. The machine learning algorithms may be especiallyuseful when applied to the stored physiological parameter data toprovide information on long-term trends, and to yield personalizedmeasurement data that are wirelessly sent back to the smart-phone.

The machine learning algorithms may also use the received and storeddata regarding one or a combination of the parameters measured todetermine health conditions or clinical insights (examples of which arelisted below in Table 2) relevant to the individual subject. Predictionsregarding future health may be made.

TABLE 2 1. COPD (Chronic Obstructive Pulmonary Disease) 2. CongestiveHeart Failure (CHF) 3. Cardiovascular diseases 4. Cardiac Arrhythmia a.Atrial Fibrillation (from ECG) b. Ventricular Tachycardia (leading toVentricular Fibrillation) 5. Stress Level 6. Sleep Apnea and Hypopnea 7.Personalized Meal Recommendation 8. Bodyweight Regulation 9.Pre-diabetic/Diabetic Stages 10. Hypothermia and Fever 11. InvoluntaryFall and Seizure 12. Cholesterol Level 13. Hypertension 14. Dehydration

Specialized, in some cases unique, algorithms may be used to provide thedeterminations, insights, and predictions. Table 3 lists examples ofsome of the types of specialized algorithms envisaged. In someembodiments, the “normal” parameter ranges relative to which thewearer's parameters are compared may be customized according to sex,race, weight, height, and/or other characteristics. Data may be analyzedover time and presented in a way that a user can monitor the progress ofhis/her health status for a given set of parameters.

TABLE 3 SpO2 extraction algorithm Heart rate extraction algorithm Heartrate variability extraction algorithm Blood pressure extractionalgorithm Respiratory rate extraction algorithm Blood glucose levelextraction algorithm Desaturation index computation algorithm Cardiacindex computation algorithm Apnea Hypopnea index computation algorithm

As indicated by step 266 in FIG. 2, the processed parameter data, trenddata and clinical insights data (or some subset of such data) may besent from the cloud directly or indirectly to a physician at a medicalfacility authorized by the subject to receive them. Upon reviewing thedata, the physician may provide advice, guidance, education, and/orprescriptions to the patient (user). Prescriptions from the patient'sdoctor may then be wirelessly and securely sent via the cloud to apharmacy pre-selected by the subject as part of his or her personalprofile, the profile having been previously created by the subject at asecure website, accessed via the smart-phone or other computing device.Users can also update their profiles directly from a smart-phone.

As indicated by step 268, some or all of the processed data may be sentfrom the cloud directly or indirectly to family members of the user,pre-authorized to receive such data.

The user's physician, other selected health professionals, familymembers, and others, make up a specific user-defined community,authorized to access data provided by the cloud platform relating tothat user.

Analytics performed in the cloud can also provide long-term trends forvital parameters and clinical insights to a subject. These long-termtrends consist of vital parameters measured over the course of manymonths or event years that is displayed in a receiver like asmart-phone, a tablet, or a computer.

As indicated by step 272, the analytics carried out at the cloud mayresult in suggestions, transmitted back to the user via the smart-phone,for adjusting the sequence and/or frequency of measurements ofparticular parameters. The system may even request additionalmeasurements of the same or other parameters if the previousmeasurements deviate from the predefined user specific range. Forinstance, an elevated temperature can trigger the automatic measurementof blood pressure, ECG, oxygen saturation, etc.

In this way and others discussed above or readily envisaged in the lightof this disclosure, the eco-system can be adaptive, responding tocurrent measurement data in the light of past data from the same subjectand/or other comparable subjects, whether to appropriately instigatefuture measurements, inform the subject of trends, or to add to thephysician's knowledge base enabling more effective guidance andtreatment.

Additional Examples and Details (1) Hardware Embedded Algorithms forReal-Time Vital Signal Processing

Unique mathematical algorithms will process the raw PPG signal generatedby the LED/Photo-Diode/AFE

SpO2 extraction algorithm

Heart rate extraction algorithm

Heart rate variability extraction algorithm

Blood pressure extraction algorithm

Respiratory rate extraction algorithm

Blood glucose level extraction algorithm

Desaturation index computation algorithm

Cardiac index computation algorithm

Apnea Hypopnea index computation algorithm

(2) Application for Smart Phone, Tablets, Laptop as User Interface, Dataand Alerts Display Alert System

The alert system has different severity level visualized by differentcolors

Color green means a specific vital parameter is within the normal range

Color yellow means that a specific vital parameter has exceeded thenormal bounds but within a critical range

Color orange means that a specific vital parameter has exceeded thecritical bounds but still below the life-threatening range

Color red means a specific vital parameter has exceeded thelife-threatening bounds and an emergency call (such as, the 911 call inUS) is automatically initiated.

A red alert is automatically issued for any life-threatening situation.In this case, a central monitoring facility first tries to establish acontact with the user, and upon no response, an emergency call (such asthe 911 in US) is issued with a message about the location of thepatient and the specific body parameter(s) in question. This will ensurethe correct paramedic team with the correct equipment arrive at thescene on time and well prepared to save the life of the patient. The redalert is handled in an automatic way to address cases where a patient isunconscious and cannot make an emergency call (such as, 911) or evenexpress him/herself. The user can also issue a red alert if a vitalparameter is in a life-threatening range and the system has not yetissued a 911 call.

User Interface

User can enter personal information called user profile

User can request instant measurement of specific vital parameter

User can request to view trend data

(3) Analytics: Software and Machine Learning Algorithms for Data Patternand Trend Analysis

The cloud-based analytics platform allows for the secured collection andlong-term hosting of all personalized vital parameter data. It allowsfor

The creation of new multi-parameter machine learning algorithms toautomatically derive the current state of various physiological vitalparameters [002] and related health conditions for an individual user

Automatic derivation of deviations from the baseline for each of theseparameters

Automatic derivation of a long-term trend for each of the vitalparameters

Automated alerts that go out from the cloud back to the users, familyand medical support staff

Derivation of gradual changes in baselines only observed over longperiods of time

Prediction of future health-critical events for an individual user basedon her own health data points and a population of health data (from apopulation bucket of individuals similar to the user in context)annotated with respective health-critical events

Special software algorithms developed to use the incoming vital signdata or combination of multiple vital signs data to provide long-termtrends and insights for various health conditions.

Personalized subject vital sign monitoring: Other embedded algorithmswill study a subject body to adjust the sequence or frequency ofmeasurements of vital parameters. This specific data is personalized toa subject's health status.

Derivation of secondary health insights (such as body weight, bodyhydration level) from the primary vital parameters.

Creation of a personalized scoring and recommendation of food itemsbased on their impact on various vital parameters.

Derivation of functioning status and health of major organs (such asliver, pancreas, and kidney) from the primary vital parameters measured.For liver health, enzyme levels in the blood critical for properfunctioning of this organ can be detected, the parameters to track areAspartate Aminotransferase (AST), Alanine Aminotransferase (ALT),alkaline phosphatase, bilirubin, albumin and total protein. For kidneyhealth, the parameters to track are Blood Urea Nitrogen (BUN),creatinine, estimated glomerular filtration rate, and for the pancreashealth, the important parameters are Amylase, Lipase and Calcium.

Derivation of various vitamin levels in blood from the primary vitalparameters measured.

Derivation of blood parameters possibly indicating an elevated risk ofpresence of some form of cancer cells on the body from primary vitalparameters measured. Some specific blood parameters include alkalinephosphatase, Lactate dehydrogenase (LDH), carcinoembryonic antigen(CEA), and prostate-specific antigen (PSA).

Derivation of other blood parameters, such as blood albumin level,amount and changes in Flavin, which can be useful in determining changesin different enzymatic levels, blood pH levels and anemic conditions.Changes in lipid levels will be used to show the trend of arterialblockages.

(4) Personalized Health Monitoring System Adaptive to IndividualPhysique and Lifestyle (PHMSYSTEM):

An individual is many ways different physically compared to every otherperson and also the lifestyle choices of that person over a period oftime and the changes thereof reflect on all vital health parametersmeasured instantaneously and/or over a time period. The apparatusdescribed in this patent when used by an individual, “learns” about theuser's unique physique and lifestyle choices over a period of time andadjusts its pattern of measurement of vital parameters. These changes inmeasurement patterns over time affect the overall operating efficiencyof the apparatus (such as battery power consumption and heating). Thesechanges help the apparatus become more adaptive to an individual withthe continuous use of it and “blends” into the unique patterns of lifeof that individual. The enhancement of the quality of life of any givenindividual is a key outcome of the individual adaptive nature of thissystem. The apparatus is capable of being set into different operatingmodes such as an adaptive mode (as described above), a traditional modewhere every measurement occurs at certain frequency, and a continuousmode where the apparatus keeps taking measurements on a continuousbasis.

(5) Individualized Food and Nutrition with the Help of Adaptive PersonalHealth Monitoring

Food and nutrition are keys to status of health of an individual. Anindividually adaptive personalized health monitoring system is at thecenter of personalization and individualization of nutrition. Theproposed apparatus starts to “learn” about the effects of various fooditems on the measured vital health parameters of an individual as soonas the individual starts to use it. Over a period of time the apparatusacquires adequate “knowledge” of impact of various food items on theindividual's overall wellbeing and estimates trend of health based onthe eating habit and recommendations on how to improve it.

(6) Continuous and Adaptive Health Monitoring as a Service (CAHMaaS)

While most healthy people do not monitor their vital data at all betweenyearly medical checkups and various medical appointments, thosesuffering from non-life threatening chronic disease requiring continuousmonitoring are not as consistent in doing so as recommended by medicalprofessionals. CAHMaaS® offer the possibility to continuously monitorone's vital data through the use of a wearable device that measuresvital data and saves them in a secured cloud server. The collected dataare then analyzed through advanced analytics to offer the users realtime clinical insights, adapted to the specific conditions of each userthrough the use of machine learning algorithms. The use of CAHMaaS®requires monthly and/or yearly subscription as well as the ownership ofa device providing the required measurements. CAHMaaS® is in the contextof Internet-of-life or an embodiment of Internet-of-life. The servicebased health monitoring system is at the heart of individualadaptiveness of the device to every user's unique physique and lifestyleover a period of time. The individualized adaptive system would helpcreate a tailored ecosystem for individual consumer, the ecosystem mightcomprise of many things like personal wellness program, customizednutrition etc, the “tailored ecosystem” as one key application as adirect result of CAHMaas.

(7) Sensors Central Device at Wrist (PPG)

(1) LED (optical signal transmitter)

(2) Photodiode (optical signal receiver)

(3) Analog Module (signal amplification and A/D conversion)

(4) MicroProcessing Unit (finite state machine, integer/FP units, datapath)

(5) Memory

(6) Host Controller Interface (HCI)

(7) Low-power Bluetooth Interface

(8) Three-axis Accelerometer (3D positioning)

(9) Thermistor/Thermopile

(10) Battery and charger unit

Remote Device at Chest (ECG)

-   -   The chest apparatus collects ECG signals that will be wirelessly        sent to the central apparatus for further processing and        storage.

Remote Device at Earlobe

-   -   The earlobe apparatus is used to collect data that gets        wirelessly sent to the central apparatus to calibrate other        vital signs for better accuracy.

Remote Device at Finger Tip

-   -   The finger tip apparatus is used to collect data that gets        wirelessly sent to the central apparatus to calibrate other        vital signs for better accuracy.

Remote Device at Leg

-   -   The three-axis accelerometer is strapped at the bottom leg part        to monitor the leg movements to process a 3-dimensional position        of the user that is wirelessly communicated to the central        apparatus

Remote Device at Forehead

-   -   The forehead apparatus is used to collect data that is        wirelessly sent to the central apparatus to calibrate other        vital signs for better accuracy.

The above-described embodiments should be considered as examples of thepresent invention, rather than as limiting the scope of the invention.Various modifications of the above-described embodiments of the presentinvention will become apparent to those skilled in the art from theforegoing description and accompanying drawings.

What is claimed is:
 1. A cloud-based analytical platform for health datapattern and trend analysis, the platform configured to: receive, from atransmitter of a portable device, processed data derived fromphysiological data measurements gathered from sensors worn by a user,the sensors being communicatively coupled to the portable device;analyze the received processed data; and transmit the results of theanalysis to at least one of the portable device and an authorizedhealthcare entity; wherein the analyzing of the received processed datacomprises: using multi-parameter machine learning algorithms toautomatically derive the current state of one or more physiologicalvital parameters and related health conditions for an individual user;automatically deriving deviations from baseline for each of the one ormore physiological vital parameters of the user; and automaticallyderiving a long-term trend for each of the one or more physiologicalvital parameters of the user.
 2. The cloud-based analytical platform ofclaim 1, wherein in response to the analyzing of the received processeddata, the platform: generates automated alerts; and transmits the alertsto at least one of the user, a family member of the user, and a medicalsupport person.
 3. The cloud-based analytical platform of claim 1,wherein the analyzing further comprises: predicting futurehealth-critical events for the user based on the user's own health datapoints and a population of health data (from a population bucket ofindividuals similar to the user in context) annotated with relevanthealth-critical events.
 4. The cloud-based analytical platform of claim1, wherein the analyzing further comprises: deriving gradual changes inbaselines for the user, observed over long periods of time.
 5. Thecloud-based analytical platform of claim 1, wherein the analyzingfurther comprises: deriving insights into a health condition experiencedby the user.
 6. The cloud-based analytical platform of claim 5, whereinthe health condition relates to status and health of a major organ. 7.The cloud-based analytical platform of claim 6, wherein the major organis the liver, and wherein the sensors measure at least one ofAminotransferase (AST), Alanine Aminotransferase (ALT), alkalinephosphatase, bilirubin, albumin and total protein.
 8. The cloud-basedanalytical platform of claim 6, wherein the major organ is a kidney, andwherein the sensors measure at least one of Blood Urea Nitrogen (BUN),creatinine, estimated glomerular filtration rate.
 9. The cloud-basedanalytical platform of claim 6, wherein the major organ is the pancreas,and wherein the sensors measure at least one of Amylase, Lipase andCalcium.
 10. The cloud-based analytical platform of claim 5, wherein thehealth condition relates to cancer, and wherein the sensors measureblood parameters including at least one of alkaline phosphatase, Lactatedehydrogenase (LDH), carcinoembryonic antigen (CEA), andprostate-specific antigen (PSA).
 11. The cloud-based analytical platformof claim 5, wherein the health condition relates to arterial blockage,and wherein the sensors measure lipid levels.
 12. The cloud-basedanalytical platform of claim 1, wherein in response to the analyzing ofthe received processed data, the platform transmits instructions to theportable device requesting adjustment of the physiological datameasurement for the user.
 13. The cloud-based analytical platform ofclaim 7, wherein the adjustment comprises changing at least one of asequence and a frequency of the data measurements for the user.
 14. Amethod of operating a cloud-based analytical platform for health datapattern and trend analysis, the method comprising: the cloud-basedplatform receiving, from a transmitter of a portable device, processeddata derived from physiological data measurements gathered from sensorsworn by a user, the sensors being communicatively coupled to theportable device; the cloud-based platform analyzing the receivedprocessed data; and the cloud-based platform transmitting the results ofthe analysis to at least one of the portable device and an authorizedhealthcare entity; wherein the analyzing of the received processed datecomprises: using multi-parameter machine learning algorithms toautomatically derive the current state of one or more physiologicalvital parameters and related health conditions for an individual user;automatically deriving deviations from baseline for each of the one ormore physiological vital parameters of the user; and automaticallyderiving a long-term trend for each of the one or more physiologicalvital parameters of the user.